Deconvolution.jl¶
Introduction¶
Deconvolution.jl provides a set of functions to deconvolve digital signals, like images or time series. This is written in Julia, a modern highlevel, highperformance dynamic programming language designed for technical computing.
Installation¶
Deconvolution.jl
is available for Julia 0.6 and later versions, and can be
installed with Julia builtin package manager. In a Julia session
run the command
julia> Pkg.update()
julia> Pkg.add("Deconvolution")
Older versions are also available for Julia 0.4 and 0.5.
Usage¶
Currently Deconvolution.jl
provides only one methd, but others will
hopefully come in the future.
wiener
function¶

wiener
(input, signal, noise[, blurring])¶
The Wiener deconvolution attempts at reducing the noise in a digital signal by suppressing frequencies with low signaltonoise ratio. The signal is assumed to be degraded by additive noise and a shiftinvariant blurring function.
Theoretically, the Wiener deconvolution method requires the knowledge of the original signal, the blurring function, and the noise. However, these conditions are difficult to met (and, of course, if you know the original signal you do not need to perform a deconvolution in order to recover the signal itself), but a strenght of the Wiener deconvolution is that it works in the frequency domain, so you only need to know with good precision the power spectra of the signal and the noise. In addition, most signals of the same class have fairly similar power spectra and the Wiener filter is insensitive to small variations in the original signal power spectrum. For these reasons, it is possible to estimate the original signal power spectrum using a representative of the class of signals being filtered.
For a short review of the Wiener deconvolution method see http://www.dmf.unisalento.it/~giordano/allow_listing/wiener.pdf and references therein.
The wiener()
function can be used to apply the Wiener deconvolution method
to a digital signal. The arguments are:
input
: the digital signalsignal
: the original signal (or a signal with a likely similar power spectrum)noise
: the noise of the signal (or a noise with a likely similar power spectrum)blurring
(optional argument): the blurring kernel
All arguments must be arrays, all with the same size, and all of them in the
time/space domain (they will be converted to the frequency domain internally
using fft
function). Argument noise
can be also a real number, in which
case a constant noise with that value will be assumed (this is a good
approximation in the case of white noise).
Examples¶
Wiener deconvolution¶
Noisy time series¶
This is an example of application of the Wiener deconvolution to a time series.
We first construct the noisy signal:
using LombScargle, Deconvolution, Plots
t = linspace(0, 10, 1000) # observation times
x = sinpi(t) .* cos.(5t) . 1.5cospi.(t) .* sin.(2t) # the original signal
n = rand(length(x)) # noise to be added
y = x + 3(n  mean(n)) # observed noisy signal
In order to perform the Wiener deconvolution, we need a signal that has a power spectrum similar to that of the original signal. We can use the Lomb–Scargle periodogram to find out the dominant frequencies in the observed signal, as implemented in the the Julia package LombScargle.jl.
# LombScargle periodogram
p = lombscargle(t, y, maximum_frequency=2, samples_per_peak=10)
plot(freqpower(p)...)
After plotting the periodogram you notice that it has three peaks, one in each
of the following intervals: \([0, 0.5]\), \([0.5, 1]\), \([1,
1.5]\). Use the LombScargle.model
function to create the bestfitting
Lomb–Scargle model at the three best frequencies, that can be found with the
findmaxfreq
function (see the manual at http://lombscarglejl.readthedocs.io/
for more details):
m1 = LombScargle.model(t, y, findmaxfreq(p, [0, 0.5])[1]) # first model
m2 = LombScargle.model(t, y, findmaxfreq(p, [0.5, 1])[1]) # second model
m3 = LombScargle.model(t, y, findmaxfreq(p, [1, 1.5])[1]) # third model
Once you have these three frequencies, you can deconvolve y
by feeding
wiener()
with a simple signal that is the sum of these three models:
signal = m1 + m2 + m3 # signal for `wiener`
noise = rand(length(y)) # noise for `wiener`
polished = wiener(y, signal, noise)
# Compare...
plot(t, x, size=(900, 600), label="Original signal", linewidth=2)
plot!(t, y, label="Observed signal") # ...original and observed signal
plot(t, x, size=(900, 600), label="Original signal", linewidth=2)
plot!(t, polished, label="Recovered with Wiener") # ...original and recovered signal
plot!(t, signal, label="Lomb–Scargle model") #...and best fitting Lomb–Scargle model
Note that the signal recovered with the Wiener deconvolution is generally a good improvement with respect to the bestfitting Lomb–Scargle model obtained using a few frequencies.
With realworld data the Lomb–Scargle periodogram may not work as good as in
this toyexample, but we showed a possible strategy to create a suitable signal
to use with wiener()
function.
Blurred image¶
Here is an example of use of wiener()
function to perform the Wiener
deconvolution of an image, degraded with a blurring function and an additive
noise.
using Images, TestImages, Deconvolution, ImageView
# Open the test image
img = float(data(testimage("cameraman")))'
# Create the blurring kernel in frequency domain
x = hcat(ntuple(x > collect((1:512)  257), 512)...)
k = 0.001
blurring_ft = exp.(k*(x .^ 2 + x' .^ 2).^(5//6))
# Create additive noise
noise = rand(size(img))
# Fourier transform of the blurred image, with additive noise
blurred_img_ft = fftshift(blurring_ft) .* fft(img) + fft(noise)
# Get the blurred image from its Fourier transform
blurred_img = real(ifft(blurred_img_ft))
# Get the blurring kernel in the space domain
blurring = ifft(fftshift(blurring_ft))
# Polish the image with Deconvolution deconvolution
polished = wiener(blurred_img, img, noise, blurring)
# Wiener deconvolution works also when you don't have the real image and noise,
# that is the most common and useful case. This happens because the Wiener
# filter only cares about the power spectrum of the signal and the noise, so you
# don't need to have the exact signal and noise but something with a similar
# power spectrum.
img2 = float(data(testimage("livingroom"))) # Load another image
noise2 = rand(size(img)) # Create another additive noise
# Polish the image with Deconvolution deconvolution
polished2 = wiener(blurred_img, img2, noise2, blurring)
# Compare...
view(img) # ...the original image
view(blurred_img) # ...the blurred image
view(polished) # ...the polished image
view(polished2) # ...the second polished image
Development¶
The package is developed at https://github.com/JuliaDSP/Deconvolution.jl. There you can submit bug reports, propose new deconvolution methods with pull requests, and make suggestions. If you would like to take over maintainership of the package in order to further improve it, please open an issue.
License¶
The Deconvolution.jl
package is licensed under the MIT “Expat” License. The
original author is Mosè Giordano.